Automated assistants with conference capabilities

ABSTRACT

Techniques are described related to enabling automated assistants to enter into a “conference mode” in which they can “participate” in meetings between multiple human participants and perform various functions described herein. In various implementations, an automated assistant implemented at least in part on conference computing device(s) may be set to a conference mode in which the automated assistant performs speech-to-text processing on multiple distinct spoken utterances, provided by multiple meeting participants, without requiring explicit invocation prior to each utterance. The automated assistant may perform semantic processing on first text generated from the speech-to-text processing of one or more of the spoken utterances, and generate, based on the semantic processing, data that is pertinent to the first text. The data may be output to the participants at conference computing device(s). The automated assistant may later determine that the meeting has concluded, and may be set to a non-conference mode.

BACKGROUND

Humans may engage in human-to-computer dialogs with interactive softwareapplications referred to herein as “automated assistants” (also referredto as “chatbots,” “interactive personal assistants,” “intelligentpersonal assistants,” “personal voice assistants,” “conversationalagents,” etc.). For example, humans (which when they interact withautomated assistants may be referred to as “users” or, in the context ofa meeting, “participants”) may provide commands, queries, and/orrequests (collectively referred to herein as “queries”) using free formnatural language input which may be vocal utterances converted into textand then processed, and/or by typed free form natural language input.Automated assistants are typically invoked using predetermined vocalutterances (e.g., “OK Assistant”) and often perform various types ofprocessing, such as speech-to-text processing, natural languageprocessing, and/or semantic processing, only on those vocal utterancesthat immediately follow an invocation phrase.

During a meeting involving multiple human participants there is often anactive or passive participant, sometimes referred to as a “secretary,”that takes notes about the meeting and shares those notes (e.g., as asummary of “action items” and/or “topics discussed”) with the meetingparticipants. Additionally or alternatively, one or more meetingparticipants may take their own notes during the meeting. In eithercase, with notetaking it is likely that some information discussedduring the meeting will be lost. Although a stenographer could beengaged to generate a full or as-full-as-possible written transcript ofthe meeting, stenography can be expensive and/or impractical for routineor informal meetings.

It is also common during meetings for participants to operate computingdevices to augment the meeting with information. In some cases one ormore participants may project or otherwise present a series of slides toguide discussion. As another example, when questions are raised (“whatflights are cheapest?”, “what will the weather be like when we'rethere?”, “what seats are available?”, etc.), one or more participantsmay manually operate a computing device such as their mobile phone toperform an Internet search seeking responsive information that they canthen convey to the group. These searches may interrupt the flow of themeeting and/or cause the searching participant to miss discussion whilethey perform their research.

SUMMARY

Techniques are described herein for enabling automated assistants toenter into a “conference mode” in which they can “participate” inmeetings between multiple human participants and perform variousfunctions described herein. In various implementations, an automatedassistant configured with selected aspects of the present disclosure mayoperate at least in part on what will be referred to herein as a“conference computing device.” A conference computing device may be anycomputing device that is capable of executing all or part of anautomated assistant and participating in a meeting between multiplehuman participants using one or more input/output components such asspeakers, displays, and in particular, microphones. A variety ofcomputing devices may be especially suitable for use as conferencecomputing devices, such as a standalone interactive speakers, videoconference computing systems, vehicle computing systems, etc. However,any computing device with a microphone and at least one output component(e.g., audio or visual) may be used as a conference computing device.

An automated assistant configured with selected aspects of the presentdisclosure may be set to a conference mode, e.g., at the outset of amulti-participant meeting. In various implementations, the outset of themeeting may be detected based on a calendar entry and/or in response toexplicit invocation of conference mode. During the meeting the automatedassistant may perform speech-to-text processing on multiple distinctspoken utterances, notably without requiring explicit invocation of theautomated assistant prior to each of the multiple distinct spokenutterances. In some scenarios, the conference computing device may be astandalone interactive speaker or video conference computing system thatis in a room or area with some, if not all, the meeting participants.However, in other scenarios in which the multiple meeting participantsare geographically separated, automated assistants operating on multipleconference computing devices deployed at the multiple locations mayperform selected aspects of the present disclosure. Based on textgenerated from the speech-to-text processing, the automated assistantmay perform a variety of functions to improve aspects of the meeting.

In some implementations, the automated assistant may perform semanticprocessing on the text generated from one or more of the multipleutterances using speech-to-text processing. Based on this semanticprocessing, the automated assistant may present (e.g., as audio and/orvisual output) a variety of information that is pertinent to the meetingdiscussion. In some implementations, the automated assistant may performthe semantic processing and/or present the resulting information inresponse to an explicit request from a meeting participant. Additionallyor alternatively, in some implementations, the automated assistant mayperform the semantic processing and/or present the information when theautomated assistant detects a pause in the meeting conversation.

The automated assistant may perform various forms of semantic processingto achieve various goals. In some implementations, the semanticprocessing may be used to identify one or more topics of conversation,e.g., by way of a topic classifier. In some such implementations, thesetopics may be used, for instance, to generate search queries (e.g.,Internet searches), to maintain a “meeting dialog context” associatedwith the meeting discussion (which may be used for various purposes,such as disambiguating participant utterances, filling slots of tasksrequested of the automated assistant, etc.), and so forth. Inimplementations in which the automated assistant generates searchqueries and performs searches based on raised topics (or more generally,based on semantic processing performed on the participants' utterances),the automated assistant may provide, as audio and/or visual output atthe conference computing device(s), information that is responsive tothe search queries.

As a working example, suppose two meeting participants are planning aski trip, and one participant says, “We should finalize our ski trip inSwitzerland next weekend. Let's pick a resort.” After speech-to-textprocessing is performed to generate text representing this vocalutterance, the automated assistant may perform semantic processing onthe text to generate a search query that duplicates or at leastsummarizes the utterance. In some cases, the automated assistant maycombine text generated from multiple utterances from multipleparticipants into a search query. Information responsive to the searchquery may include, for instance, a list of one or more ski resorts inSwitzerland. If the conference computing device includes or has accessto a display, these results may be presented automatically on thedisplay, e.g., much like if one of the users had explicitly performedthe search. Alternatively, if the conference computing device includesor has access to a speaker, data indicative of the results may beaudibly output at the speaker, e.g., during a pause in conversation. Itshould be understood that in many implementations in which the availableoutput component is a speaker, less information may be output than ifthe output component were a display. This is because audio output may bemore distracting and/or require more time to be output than visualoutput, and it may be beneficial to avoid interrupting the flow of themeeting.

In some implementations, information that is output to the participantsand/or the ongoing meeting dialog context may be used to performadditional semantic processing on subsequent utterances. For example,and continuing the working example, one or more of the participants mayask follow up questions inspired by the presented results of the Swissski resort search query. Suppose a user asks, “how is the skiing there?”In isolation this question may be too ambiguous because the word “there”fails to identify a target resort. However, as alluded to above,automated assistants configured with selected aspects of the presentdisclosure may be configured to maintain a meeting dialog context thatmaintains one or more topics of discussion and/or information that hasbeen output by the automated assistant. In this example, the automatedassistant may disambiguate “there” to, for instance, the top rankingSwiss ski resort that was presented previously. Or, had the user saidsomething like, “Zermatt looks interesting, how is the skiing there?”,then the target ski resort may instead be “Zermatt.” In any case, theautomated assistant may then generate and submit a search query thatseeks information (e.g., ski reports, snow reports, user reviews, etc.)about ski quality at the target resort.

Similar techniques might be employed to generate a suitable search queryif the participant were instead to ask, “What will the weather be like?”Once again this statement is too ambiguous in isolation to generate ameaningful weather search query. However, based on the persisted meetingdialog context, the automated assistant may be able to infer that thelocation to use in the weather search query is the top-ranking resortthat was presented previously, and that a time to use in the weathersearch query is “next weekend.” Thus, the automated assistant may searchthe weather at the top-ranking resort for the following weekend, and maypresent the results to the participants. This back-and-forth between theparticipants and/or the automated assistant may continue for other typesof information, such as making travel arrangements (e.g., trainschedules could be presented), purchasing ski passes, etc.

In some implementations, an automated assistant configured with selectedaspects of the present disclosure may be configured to generate, basedon the multiple distinct utterances detected during the meeting, ameeting summary. In various implementations, the meeting summary maytake the form of a document (e.g., textual and/or graphical) thatincludes pieces of information such as one or more topics detected bythe automated assistant from the meeting discussion, one or moreoutcomes of the meeting detected by the automated assistant from themeeting discussion, a textual transcript of at least some of themultiple distinct spoken utterances, information about participants inthe meeting (e.g., some automated assistants may be able to match voiceswith voice profiles associated with particular people), and so forth.This meeting summary may be stored, transmitted, and/or shared, e.g., bythe automated assistant, to/with one or more of the meetingparticipants. In some implementations, the meeting summary may beassociated with a calendar entry that was created to schedule themeeting.

In some implementations, an automated assistant configured with selectedaspects of the present disclosure may be configured to utilizeinformation generated during one meeting using techniques describedherein (e.g., the meeting dialog context, the meeting summary) toperform various functions in a subsequent meeting, e.g., follow upmeeting. Suppose participants in a first meeting discuss a number ofaction items that need to be resolved, and these action items aredetected by a participating automated assistant and used, for instance,to generate a meeting summary for the first meeting. At a follow upsecond meeting, the automated assistant may use information from thefirst meeting's summary and/or information from a meeting dialog contextstored from the first meeting to perform various functions describedabove. For example, suppose a user asks, “OK, what were the action itemsfrom last meeting?” The automated assistant may retrieve and outputthese action items, e.g., as an audible list or on a display. In someimplementations, the participants of the second meeting may instruct theautomated assistant that one or more of the action items have beencompleted, or the automated assistant may detect that one or more actionitems was completed based on semantic processing of participant vocalutterances during the meeting.

In some implementations, the automated assistant may be able to detecton its own whether an action item was completed since the first meeting.For example, suppose, after the first meeting, one of the participantsengages with an automated assistant to address an action item (e.g.,purchase disposable plate ware). In the second meeting, that action itemmay not be presented by the participating automated assistant because ithas already been addressed. Additionally or alternatively, the actionitem may be presented as “complete.”

In various implementations, automated assistants may determine that twoor more meetings are related (e.g., as initial and follow up meetings)in various ways. In some implementations, participants may schedule themeetings, e.g., using electronic calendars, and may explicitly link themeetings. Additionally or alternatively, in some implementations, theautomated assistant may automatically detect that two or more meetingsare related, e.g., based on titles given to the meetings, overlap inparticipants in the meeting, documents associated with the meetings, andso forth. In some implementations in which a document (e.g., a calendarentry or an agenda attached thereto) is associated with a meeting, theautomated assistant may generate an initial meeting dialog context basedon the associated document.

As noted above, audio output generated by the automated assistant duringa meeting may be far more distracting than visual output, whichparticipants can ignore. Accordingly, in various implementations, anautomated assistant may identify an output modality used by one or moreof the conference computing devices that is perceptible to the multiplemeeting participants. The automated assistant may then output datapertinent to multiple distinct spoken utterances during the meeting at afrequency that is selected based on the identified output modality. Forexample, if the conference computing device is a standalone interactivespeaker without a display, the automated assistant may provide output(e.g., search results, action item statuses, etc.) less frequently thanif the conference computing device included a display. As a specificexample in which the output modality is determined to be audio output ina vehicle driven by a driver who is also one of the meetingparticipants, the frequency at which data pertinent to multiple distinctspoken utterances is presented by the automated assistant may beselected to avoid distracting the driver.

In some implementations, a method performed by one or more processors isprovided that includes: setting an automated assistant implemented atleast in part on one or more conference computing devices to aconference mode in which the automated assistant performs speech-to-textprocessing on multiple distinct spoken utterances without requiringexplicit invocation of the automated assistant prior to each of themultiple distinct spoken utterances, wherein the multiple distinctspoken utterances are provided by multiple participants during a meetingbetween the multiple participants; automatically performing, by theautomated assistant, semantic processing on first text generated fromthe speech-to-text processing of one or more of the multiple distinctspoken utterances, wherein the semantic processing is performed withoutexplicit participant invocation; generating, by the automated assistant,based on the semantic processing, data that is pertinent to the firsttext, wherein the data is output to the multiple participants at one ormore of the conference computing devices while the automated assistantis in conference mode; determining, by the automated assistant, that themeeting has concluded; and based on the determining, setting theautomated assistant to a non-conference mode in which the automatedassistant requires invocation prior to performing speech-to-textprocessing on individual spoken utterances.

These and other implementations of technology disclosed herein mayoptionally include one or more of the following features.

In various implementations, the data may be output to the multipleparticipants as natural language output from the automated assistant viaa speaker of one or more of the conference computing devices. In variousimplementations, the data may be output to the multiple participants viaone or more displays that are visible to the multiple participants.

In various implementations, the determining includes: receiving, by theautomated assistant, from one of the multiple participants, a spokeninvocation that indicates the meeting has concluded; or determining thata current time matches a scheduled end time of the meeting.

In various implementations, the automated assistant may be set toconference mode in response to a spoken invocation that indicates themeeting has begun or an explicit command to enter conference mode. Invarious implementations, the method may further include performingadditional semantic processing on second text generated fromspeech-to-text processing of one or more of the multiple spokenutterances, wherein the additional semantic processing is performedbased at least in part on the data that is pertinent to the first text.In various implementations, the additional semantic processing includesdisambiguation of one or more tokens of the second text based on thedata that is pertinent to the first text.

In various implementations, the method may further include generating,by the automated assistant based on the multiple distinct utterances, ameeting summary, wherein the meeting summary includes one or more topicsdetected by the automated assistant from the multiple distinct spokenutterances while the automated assistant was in conference mode. Invarious implementations, the meeting summary may further include one ormore outcomes of the meeting detected by the automated assistant fromthe multiple distinct spoken utterances while the automated assistantwas in conference mode. In various implementations, the meeting summaryfurther includes a textual transcript of at least some of the multipledistinct spoken utterances.

In various implementations, the method may further include: determiningthat the meeting is related to a prior meeting; and identifying, by theautomated assistant, based on information associated with the priormeeting, additional data that was generated during the prior meeting andis pertinent to the current meeting, wherein the additional data isoutput to the multiple participants at one or more of the conferencecomputing devices while the automated assistant is in conference mode.

In various implementations, the method may further include: identifyingan output modality used by one or more of the conference computingdevices that is perceptible to the multiple participants; and outputtingdata pertinent to the multiple distinct spoken utterances at a frequencythat is related to the identified output modality. In variousimplementations, the output modality includes audio output in a vehicledriven by a driver who is also one of the participants, and thefrequency at which the data pertinent to the multiple distinct spokenutterances is output is selected to avoid distracting the driver.

In addition, some implementations include one or more processors of oneor more computing devices, where the one or more processors are operableto execute instructions stored in associated memory, and where theinstructions are configured to cause performance of any of theaforementioned methods. Some implementations also include one or morenon-transitory computer readable storage media storing computerinstructions executable by one or more processors to perform any of theaforementioned methods.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in whichimplementations disclosed herein may be implemented.

FIG. 2A, FIG. 2B, FIG. 2C, and FIG. 2D depict one example of howtechniques described herein may be employed in a particular scenario, inaccordance with various implementations.

FIG. 3 demonstrates another example of how techniques described hereinmay be employed in another scenario, in accordance with variousimplementations.

FIG. 4 depicts a flowchart illustrating an example method according toimplementations disclosed herein.

FIG. 5 illustrates an example architecture of a computing device.

DETAILED DESCRIPTION

Now turning to FIG. 1 , an example environment in which techniquesdisclosed herein may be implemented is illustrated. The exampleenvironment includes one or more client computing devices 106 _(1-N).Each client device 106 may execute a respective instance of an automatedassistant client 118. One or more cloud-based automated assistantcomponents 119, such as a natural language processor 122, may beimplemented on one or more computing systems (collectively referred toas a “cloud” computing system) that are communicatively coupled toclient devices 106 _(1-N) via one or more local and/or wide areanetworks (e.g., the Internet) indicated generally at 110.

As noted in the background, an instance of an automated assistant client118, by way of its interactions with one or more cloud-based automatedassistant components 119, may form what appears to be, from the user'sperspective, a logical instance of an automated assistant 120 with whichthe user may engage in a human-to-computer dialog. Two instances of suchan automated assistant 120 are depicted in FIG. 1 . A first automatedassistant 120A encompassed by a dashed line serves a first user (notdepicted) operating first client device 106 ₁ and includes automatedassistant client 118 ₁ and one or more cloud-based automated assistantcomponents 119. A second automated assistant 120B encompassed by adash-dash-dot line serves a second user (not depicted) operating anotherclient device 106 _(N) and includes automated assistant client 118 _(N)and one or more cloud-based automated assistant components 119. It thusshould be understood that in some implementations, each user thatengages with an automated assistant client 118 executing on a clientdevice 106 may, in effect, engage with his or her own logical instanceof an automated assistant 120. For the sakes of brevity and simplicity,the term “automated assistant” as used herein as “serving” a particularuser will refer to the combination of an automated assistant client 118executing on a client device 106 operated by the user and one or morecloud-based automated assistant components 119 (which may be sharedamongst multiple automated assistant clients 118). It should also beunderstood that in some implementations, automated assistant 120 mayrespond to a request from any user regardless of whether the user isactually “served” by that particular instance of automated assistant120.

The client devices 106 _(1-N) may include, for example, one or more of:a desktop computing device, a laptop computing device, a tabletcomputing device, a mobile phone computing device, a computing device ofa vehicle of the user (e.g., an in-vehicle communications system, anin-vehicle entertainment system, an in-vehicle navigation system), astandalone interactive speaker, a smart appliance such as a smarttelevision, and/or a wearable apparatus of the user that includes acomputing device (e.g., a watch of the user having a computing device,glasses of the user having a computing device, a virtual or augmentedreality computing device). Additional and/or alternative clientcomputing devices may be provided.

In various implementations, each of the client computing devices 106_(1-N) may operate a variety of different applications, such as acorresponding one of a plurality of message exchange clients 107 _(1-N).Message exchange clients 107 _(1-N) may come in various forms and theforms may vary across the client computing devices 106 _(1-N) and/ormultiple forms may be operated on a single one of the client computingdevices 106 _(1-N). In some implementations, one or more of the messageexchange clients 107 _(1-N) may come in the form of a short messagingservice (“SMS”) and/or multimedia messaging service (“MMS”) client, anonline chat client (e.g., instant messenger, Internet relay chat, or“IRC,” etc.), a messaging application associated with a social network,a personal assistant messaging service dedicated to conversations withautomated assistant 120, and so forth. In some implementations, one ormore of the message exchange clients 107 _(1-N) may be implemented via awebpage or other resources rendered by a web browser (not depicted) orother application of client computing device 106.

As described in more detail herein, automated assistant 120 engages inhuman-to-computer dialog sessions with one or more users via userinterface input and output devices of one or more client devices 106_(1-N). In some implementations, automated assistant 120 may engage in ahuman-to-computer dialog session with a user in response to userinterface input provided by the user via one or more user interfaceinput devices of one of the client devices 106 _(1-N). In some of thoseimplementations, the user interface input is explicitly directed toautomated assistant 120. For example, one of the message exchangeclients 107 _(1-N) may be a personal assistant messaging servicededicated to conversations with automated assistant 120 and userinterface input provided via that personal assistant messaging servicemay be automatically provided to automated assistant 120. Also, forexample, the user interface input may be explicitly directed toautomated assistant 120 in one or more of the message exchange clients107 _(1-N) based on particular user interface input that indicatesautomated assistant 120 is to be invoked. For instance, the particularuser interface input may be one or more typed characters (e.g.,@AutomatedAssistant), user interaction with a hardware button and/orvirtual button (e.g., a tap, a long tap), an oral command (e.g., “HeyAutomated Assistant”), and/or other particular user interface input.

In some implementations, automated assistant 120 may engage in a dialogsession in response to user interface input, even when that userinterface input is not explicitly directed to automated assistant 120.For example, automated assistant 120 may examine the contents of userinterface input and engage in a dialog session in response to certainterms being present in the user interface input and/or based on othercues. In some implementations, automated assistant 120 may engageinteractive voice response (“IVR”), such that the user can uttercommands, searches, etc., and the automated assistant may utilizenatural language processing and/or one or more grammars to convert theutterances into text, and respond to the text accordingly. In someimplementations, the automated assistant 120 can additionally oralternatively respond to utterances without converting the utterancesinto text. For example, the automated assistant 120 can convert voiceinput into an embedding, into entity representation(s) (that indicateentity/entities present in the voice input), and/or other “non-textual”representation and operate on such non-textual representation.Accordingly, implementations described herein as operating based on textconverted from voice input may additionally and/or alternatively operateon the voice input directly and/or other non-textual representations ofthe voice input.

Each of the client computing devices 106 _(1-N) and computing device(s)operating cloud-based automated assistant components 119 may include oneor more memories for storage of data and software applications, one ormore processors for accessing data and executing applications, and othercomponents that facilitate communication over a network. The operationsperformed by one or more of the client computing devices 106 _(1-N)and/or by automated assistant 120 may be distributed across multiplecomputer systems. Automated assistant 120 may be implemented as, forexample, computer programs running on one or more computers in one ormore locations that are coupled to each other through a network.

As noted above, in various implementations, each of the client computingdevices 106 _(1-N) may operate an automated assistant client 118. Invarious implementations, each automated assistant client 118 may includea corresponding speech capture/text-to-speech (“TTS”)/STT module 114. Inother implementations, one or more aspects of speech capture/TTS/STTmodule 114 may be implemented separately from automated assistant client118.

Each speech capture/TTS/STT module 114 may be configured to perform oneor more functions: capture a user's speech, e.g., via a microphone(which in some cases may comprise presence sensor 105); convert thatcaptured audio to text (and/or to other representations or embeddings);and/or convert text to speech. For example, in some implementations,because a client device 106 may be relatively constrained in terms ofcomputing resources (e.g., processor cycles, memory, battery, etc.), thespeech capture/TTS/STT module 114 that is local to each client device106 may be configured to convert a finite number of different spokenphrases—particularly phrases that invoke automated assistant 120—to text(or to other forms, such as lower dimensionality embeddings). Otherspeech input may be sent to cloud-based automated assistant components119, which may include a cloud-based TTS module 116 and/or a cloud-basedSTT module 117.

Cloud-based STT module 117 may be configured to leverage the virtuallylimitless resources of the cloud to convert audio data captured byspeech capture/TTS/STT module 114 into text (which may then be providedto natural language processor 122). Cloud-based TTS module 116 may beconfigured to leverage the virtually limitless resources of the cloud toconvert textual data (e.g., natural language responses formulated byautomated assistant 120) into computer-generated speech output. In someimplementations, TTS module 116 may provide the computer-generatedspeech output to client device 106 to be output directly, e.g., usingone or more speakers. In other implementations, textual data (e.g.,natural language responses) generated by automated assistant 120 may beprovided to speech capture/TTS/STT module 114, which may then convertthe textual data into computer-generated speech that is output locally.

Automated assistant 120 (and in particular, cloud-based automatedassistant components 119) may include a natural language processor 122,the aforementioned TTS module 116, the aforementioned STT module 117, adialog state tracker 124, a dialog manager 126, and a natural languagegenerator 128 (which in some implementations may be combined with TTSmodule 116), and of particular relevance to the present disclosure, aconference engine 130. In some implementations, one or more of theengines and/or modules of automated assistant 120 may be omitted,combined, and/or implemented in a component that is separate fromautomated assistant 120.

In some implementations, automated assistant 120 generates responsivecontent in response to various inputs generated by a user of one of theclient devices 106 _(1-N) during a human-to-computer dialog session withautomated assistant 120. Automated assistant 120 may provide theresponsive content (e.g., over one or more networks when separate from aclient device of a user) for presentation to the user as part of thedialog session. For example, automated assistant 120 may generateresponsive content in in response to free-form natural language inputprovided via one of the client devices 106 _(1-N). As used herein,free-form input is input that is formulated by a user and that is notconstrained to a group of options presented for selection by the user.

As used herein, a “dialog session” may include alogically-self-contained exchange of one or more messages between a userand automated assistant 120 (and in some cases, other humanparticipants) and/or performance of one or more responsive actions byautomated assistant 120. Automated assistant 120 may differentiatebetween multiple dialog sessions with a user based on various signals,such as passage of time between sessions, change of user context (e.g.,location, before/during/after a scheduled meeting, etc.) betweensessions, detection of one or more intervening interactions between theuser and a client device other than dialog between the user and theautomated assistant (e.g., the user switches applications for a while,the user walks away from then later returns to a standalonevoice-activated product), locking/sleeping of the client device betweensessions, change of client devices used to interface with one or moreinstances of automated assistant 120, and so forth. As will described inmore detail below, in some implementations, automated assistant 120 may,e.g., by way of conference engine 130, facilitate a “conference dialogsession” in which automated assistant 120 is transitioned into a“conference mode” in which it does not require explicit invocation priorto each oral (or written statement) in order to perform variousfunctions, such as natural language processing.

Natural language processor 122 (alternatively referred to as a “naturallanguage understanding engine”) of automated assistant 120 processesfree form natural language input generated by users via client devices106 _(1-N) and in some implementations may generate annotated output foruse by one or more other components of automated assistant 120. Forexample, the natural language processor 122 may process natural languagefree-form input that is generated by a user via one or more userinterface input devices of client device 106 ₁. The generated annotatedoutput may include one or more annotations of the natural language inputand optionally one or more (e.g., all) of the terms of the naturallanguage input.

In some implementations, the natural language processor 122 isconfigured to identify and annotate various types of grammaticalinformation in natural language input. For example, the natural languageprocessor 122 may include a part of speech tagger (not depicted)configured to annotate terms with their grammatical roles. For example,the part of speech tagger may tag each term with its part of speech suchas “noun,” “verb,” “adjective,” “pronoun,” etc. Also, for example, insome implementations the natural language processor 122 may additionallyand/or alternatively include a dependency parser (not depicted)configured to determine syntactic relationships between terms in naturallanguage input. For example, the dependency parser may determine whichterms modify other terms, subjects and verbs of sentences, and so forth(e.g., a parse tree)—and may make annotations of such dependencies.

In some implementations, the natural language processor 122 mayadditionally and/or alternatively include an entity tagger (notdepicted) configured to annotate entity references in one or moresegments such as references to people (including, for instance, literarycharacters, celebrities, public figures, etc.), organizations, locations(real and imaginary), and so forth. In some implementations, data aboutentities may be stored in one or more databases, such as in a knowledgegraph (not depicted). In some implementations, the knowledge graph mayinclude nodes that represent known entities (and in some cases, entityattributes), as well as edges that connect the nodes and representrelationships between the entities. For example, a “banana” node may beconnected (e.g., as a child) to a “fruit” node,” which in turn may beconnected (e.g., as a child) to “produce” and/or “food” nodes. Asanother example, a restaurant called “Hypothetical Café” may berepresented by a node that also includes attributes such as its address,type of food served, hours, contact information, etc. The “HypotheticalCafé” node may in some implementations be connected by an edge (e.g.,representing a child-to-parent relationship) to one or more other nodes,such as a “restaurant” node, a “business” node, a node representing acity and/or state in which the restaurant is located, and so forth.

The entity tagger of the natural language processor 122 may annotatereferences to an entity at a high level of granularity (e.g., to enableidentification of all references to an entity class such as people)and/or a lower level of granularity (e.g., to enable identification ofall references to a particular entity such as a particular person). Theentity tagger may rely on content of the natural language input toresolve a particular entity and/or may optionally communicate with aknowledge graph or other entity database to resolve a particular entity.

In some implementations, the natural language processor 122 mayadditionally and/or alternatively include a coreference resolver (notdepicted) configured to group, or “cluster,” references to the sameentity based on one or more contextual cues. For example, thecoreference resolver may be utilized to resolve the term “there” to“Hypothetical Café” in the natural language input “I liked HypotheticalCafé last time we ate there.”

In some implementations, one or more components of the natural languageprocessor 122 may rely on annotations from one or more other componentsof the natural language processor 122. For example, in someimplementations the named entity tagger may rely on annotations from thecoreference resolver and/or dependency parser in annotating all mentionsto a particular entity. Also, for example, in some implementations thecoreference resolver may rely on annotations from the dependency parserin clustering references to the same entity. In some implementations, inprocessing a particular natural language input, one or more componentsof the natural language processor 122 may use related prior input and/orother related data outside of the particular natural language input todetermine one or more annotations.

In some implementations, dialog state tracker 124 may be configured tokeep track of a “dialog state” that includes, for instance, a beliefstate of a one or more users' goals (or “intents”) over the course of ahuman-to-computer dialog session, across multiple dialog sessions,and/or during a conference dialog session. In determining a dialogstate, some dialog state trackers may seek to determine, based on userand system utterances in a dialog session, the most likely value(s) forslot(s) that are instantiated in the dialog. Some techniques utilize afixed ontology that defines a set of slots and the set of valuesassociated with those slots. Some techniques additionally oralternatively may be tailored to individual slots and/or domains. Forexample, some techniques may require training a model for each slot typein each domain.

Dialog manager 126 may be configured to map a current dialog state,e.g., provided by dialog state tracker 124, to one or more “responsiveactions” of a plurality of candidate responsive actions that are thenperformed by automated assistant 120. Responsive actions may come in avariety of forms, depending on the current dialog state. For example,initial and midstream dialog states that correspond to turns of a dialogsession that occur prior to a last turn (e.g., when the ultimateuser-desired task is performed) may be mapped to various responsiveactions that include automated assistant 120 outputting additionalnatural language dialog. This responsive dialog may include, forinstance, requests that the user provide parameters for some action(i.e., fill slots) that dialog state tracker 124 believes the userintends to perform. In some implementations, responsive actions mayinclude actions such as “request” (e.g., seek parameters for slotfilling), “offer” (e.g., suggest an action or course of action for theuser), “select,” “inform” (e.g., provide the user with requestedinformation), “no match” (e.g., notify the user that the user's lastinput is not understood), and so forth.

Conference engine 130 may be configured to facilitate a “conferencemode” of automated assistant 120 that enables automated assistant 120 to“participate” in meetings between multiple human participants andperform various functions. In various implementations, automatedassistant 120 configured with selected aspects of the present disclosuremay operate at least in part on what will be referred to herein as a“conference computing device.” A conference computing device may be anycomputing device, including one or more client devices 106, that iscapable of participating in a meeting between multiple humanparticipants using one or more input/output components such as speakers,displays, and in particular, microphones. A variety of computing devicesmay be especially suitable for use as conference computing devices, suchas a standalone interactive speakers, video conference computingsystems, vehicle computing systems, etc. However, any computing devicewith a microphone and at least one output component (e.g., audio orvisual) may be used as a conference computing device.

In various implementations, conference engine 130 may be configured setautomated assistant 120 to the aforementioned “conference mode” to causeautomated assistant 120 to perform speech-to-text processing (e.g., byway of STT 117) on multiple distinct spoken utterances without requiringexplicit invocation of automated assistant 120 prior to each of themultiple distinct spoken utterances. In many cases, the multipledistinct spoken utterances may be provided by multiple participantsduring a meeting or conference between the multiple participants. Byperforming natural language processing and other processing of spokenuser utterances without requiring explicit invocation each time,automated assistant 120 may be able to perform a variety of functionsthat may be helpful to participants of the meeting.

For example, in some implementations, while in conference mode,automated assistant 120 may be free to provide information to theparticipants that is based on the participants' discussion. Moreparticularly, in some implementations, automated assistant 120 mayautomatically (i.e., without requiring an explicit command from aparticipant) perform semantic processing (e.g., by way of naturallanguage processor 122 and/or other cloud-based automated assistantcomponents 119) on first text generated from the speech-to-textprocessing of one or more of the multiple distinct spoken utterancesprovided by the meeting participants. If automated assistant 120 werenot in conference mode, it would not perform such semantic processingwithin explicit invocation. Based on the semantic processing, automatedassistant 120 may generate data that is pertinent to the text that wassemantically processed. For example, if the text was generated from auser utterance that included a question, the text may be used togenerate a search query that automated assistant 120 submits to one ormore databases. Data responsive to the search query may then be obtainedby automated assistant 120 and output to the multiple meetingparticipants at one or more of the conference computing devices.Examples of such a scenario will be described below.

Not every participant utterance is worthy of a response by automatedassistant 120. For example, participants may engage in informal banterduring the meeting to which they may not desire automated assistant 120react. Accordingly, in various implementations, automated assistant 120may analyze various criteria to determine whether to inject intomeetings content it retrieves based on semantic processing of theparticipants' discussions. In some implementations, automated assistant120 may determine a relevancy score associated with information itobtains responsive to a participant's utterance. If the retrievedinformation has a relevancy score that satisfies some minimum relevancythreshold, automated assistant 120 may potentially incorporate theinformation into the discussion (e.g., subject to other constraintsrelated to modality described below). On the other hand, if theretrieved information has a relevancy score that fails to satisfy such athreshold, automated assistant 120 may refrain from incorporating theinformation into the meeting discussion because that information may notlikely be useful to, or well-received by, the participants.

Automated assistant 120 may perform a variety of other functions whilein conference mode to aid the meeting participants. For example,automated assistant 120 may provide audio or visual output that providesthe participants with information about an agenda, document(s), or otherinformation associated with the meeting. Suppose a meeting is scheduledusing an electronic/online calendar system, and that the calendar entryincludes a meeting agenda prepared by, for instance, one of theparticipants. Such a meeting agenda may include various information,such as topic(s) for discussion, action items and their associatedstatuses (e.g., complete or incomplete), participant identities, agendaitems subject to a vote, relationship of the current meeting to prior orfuture meetings, and so forth.

In some implementations, such meeting agenda may be displayed and/orre-displayed continuously and/or periodically displayed during themeeting. For example, in some implementations, automated assistant 120may be configured with a topic classifier that identifies, from textgenerated from participant utterances, one or more topics that areraised and/or identifies when discussion has transitioned betweendifferent topics. Such a topic classifier may employ a variety of knowntechniques of topic classification that are often used for documentclassification, such as expectation maximization, term-frequency-inversedocument frequency (“TF-IDF”), naïve Bayes classification, latentsemantic indexing, support vector machines, artificial neural networks,decision trees, concept mining, etc.

In some implementations in which the meeting agenda includes actionitems, automated assistant 120 may be configured to semantically processutterances provided by the participants during the meeting to determinewhether the action items have been addressed (e.g., resolved, delayed,modified, canceled, etc.). Automated assistant 120 may, when it displaysthe agenda, modify the displayed information about the action itemsaccordingly. One example of this is described below with respect to FIG.2C. Also, in some implementation in which a sequence of slides is beingpresented, automated assistant 120 may semantically processparticipants' utterances to automatically advance the slides through thesequence.

In some implementations, automated assistant 120 may, e.g., after beingtransitioned from the conference mode back to a non-conference or“normal” mode in which it requires explicit invocation prior tosemantically processing an utterance, generate a meeting summary. Insome implementations, the meeting summary may be similar to the meetingagenda, except that the meeting summary may be annotated based oncontent of the meeting participants' discussion learned through semanticprocessing of the meeting's discussion. Additionally or alternatively,and particularly where no meeting agenda was prepared prior to themeeting, automated assistant 120 may newly generate a meeting summarysolely based on semantic processing of the participants' discussion.

Meeting summaries generated by automated assistant 120 may include avariety of other information. In addition to or instead of informationthat might also be included in a meeting agenda, a meeting summarygenerated using techniques described herein may include topics discussed(which may be detected at least in part by way of the aforementionedtopic classifier), action items created/addressed/modified, outcomes ofthe meeting (e.g., booking a venue, purchasing tickets, vote outcomes,etc.), a partial or whole transcript of some or all participants'utterances during the meeting, a next (or follow up) meeting if theparticipants' discussed scheduling one, and so forth.

In various implementations, automated assistant 120 may determine, e.g.,by way of conference engine 130, when a meeting begins and/orconcludes—and hence, when automated assistant 120 should transitionbetween conference mode and normal mode—using a variety of cues. In someimplementations, a meeting participant may issue an explicit command,such as “Hey Assistant, let's start the meeting,” to cause automatedassistant 120 to transition into conference mode. Additionally oralternatively, in some implementations, automated assistant 120 mayinfer when to transition from normal mode to conference mode based onuser utterances. For example, automated assistant 120 may transitionfrom normal mode to conference mode when a participant says, e.g., toanother participant (and not directly to automated assistant 120),something like, “OK, let's get started” or “Let's bring this meeting toorder.” If the meeting is a type of meeting with in which formalprocedures are supposed to be followed, such as a public hearing, anon-profit board meeting, etc., then phrases that are commonly and/orofficial uttered to initiate such formal meetings may be detected andcause automated assistant 120 to transition from normal mode toconference mode. In some implementations, automated assistant 120 may beconfigured to tally votes cast by participants at such meetings.

In some implementations, automated assistant 120 may have access to oneor more electronic calendar entries that indicate a meeting is to takeplace at a particular time and/or location. In some suchimplementations, automated assistant 120 may automatically transitioninto conference mode at the meeting's scheduled starting time, and/or atsome point after the schedule starting time when automated assistant 120detects (e.g., using one or more microphones and/or cameras) that atleast some of the participants are co-present at a designated meetinglocation. Similarly, automated assistant 120 may determine when totransition from conference mode back into normal mode based on explicituser instruction (e.g., “Hey Assistant, let's end the meeting”),implicit user utterances (e.g., “Let's call it a day”), and/orformalized utterances (e.g., “This meeting is adjourned”).

There are various challenges associated with an automated assistant 120automatically incorporating content into a meeting between multiplehuman participants. If the human participants are speaking to eachother, and not to automated assistant 120, it might be distracting forautomated assistant 120 to provide content when a participant isexpecting feedback from another participant. If automated assistant 120is too quick to provide search results in response to a speaker'sutterance that includes a question (which automated assistant 120 mightsubmit as a search query), the presentation of responsive content,especially if done audibly, may be distracting and/or interrupt one ormore participants who had intended to respond to the speaker'sutterance. Moreover, if automated assistant 120 provides responsivecontent for too many participant utterances, the participants may becomedistracted and/or inundated with too much information. In other words,automated assistant 120 may become intrusive.

Accordingly, in various implementations, automated assistant 120 may beconfigured to exercise various levels of discretion when outputtingcontent to meeting participants (also referred to as “injecting contentinto the discussion”), based on a variety of cues. In someimplementations, when automated assistant 120 semantically processes aparticipant's utterance and has retrieved responsive content, automatedassistant 120 may wait for a pause in the conversation (e.g., apredetermined time interval such as five seconds, etc.) before itprovides the responsive content as output. In some such implementations,if no such pause occurs, e.g., because the meeting participants continuetheir discussion in earnest, automated assistant 120 may wait for apause or discard the responsive content, especially if automatedassistant 120 determines that the context of the discussion has changed(e.g., a new topic of discussion is detected). In some implementations,automated assistant 120 may discard such responsive content if there isno pause in the conversation for some predetermined time interval, suchas one minute, five minutes, thirty seconds, etc.

In some implementations, automated assistant 120 may exercise a level ofdiscretion when automatically injecting content into the discussion thatis commensurate with a type of output modality available to automatedassistant 120. Audible output, e.g., provided by a client device 106 inthe form of a standalone speaker or conference telephone configured withselected aspects of the present disclosure, may be distracting ifpresented too frequently. By contrast, visual output may be lessdistracting. Thus, if automated assistant 120 is able to provide visualoutput on a display, e.g., a conference television screen or evenindividual computer screens viewed by the participants, automatedassistant 120 may exercise a relatively low level of discretion whendetermining whether and/or when to output content. On the other hand, ifautomated assistant 120 is only able to provide audible output via oneor more speakers, automated assistant 120 may exercise a greater levelof discretion when determining whether and/or when to output content.

Examples described herein are primarily directed to scenarios in which aplurality of meeting participants are physically co-located with aclient device 106 such as a standalone interactive speaker and/ordisplay that operates an automated assistant 120 configured withselected aspects of the present disclosure. However, this is not meantto be limiting. Techniques described herein are equally applicable inscenarios in which meeting participants are not co-located. For example,suppose two or more participants are conducting a meeting using videoconferencing, e.g., with each user sitting in front of his or her owncomputer. In some implementations, automated assistant 120 may providethe same output to each participant on their respective screen. In otherimplementations, automated assistant 120 may provide different contentto each participant on their screen, e.g., depending on individualparticipant preferences, individual participant content (e.g., oneparticipant may be in a public place and might not want potentiallysensitive information displayed), and so forth. In scenarios in whichtwo meeting participants are not co-located and are operating clientdevices 106 with different output modalities—e.g., one audio, onevisual—automated assistant 120 may provide (or “push”) more content tobe presented to the participant with visual output capabilities than theparticipant with exclusively audio output capabilities.

FIGS. 2A-D demonstrate one example of a meeting between multipleparticipants 202 ₁₋₃ in which automated assistant 120 “participates” byway of being executed at least in part on one or more client devices 206₁₋₂. In this example, first client device 206 ₁ takes the form of astandalone interactive speaker with a microphone (not specificallydepicted) and second client device 206 ₂ takes the form of a smarttelevision with display capabilities. For this example, it can beassumed that the participants 202 ₁₋₃ scheduled the meeting using anelectronic calendar, and that there was an agenda defined by one of theparticipants, either in the calendar entry or in a separate documentattached to the calendar entry.

In FIG. 2A, a first participant 202 ₁ initiates the meeting by speakingthe utterance, “OK, Assistant, let's start the meeting.” This is anexample of an explicit command for automated assistant 120 to transitionfrom a non-conference or normal mode to the conference mode describedabove. An agenda for the meeting is displayed on second client device206 ₂, e.g., at the behest of automated assistant 120. The agendaincludes two topics: “Plan company event” and “review budget.” In someimplementations, the agenda may be displayed upon transition ofautomated assistant 120 to conference mode.

In FIG. 2B, a second participant 202 ₂ says, “We should plan the companyevent at the ball park.” Based on semantic processing of this utterance,automated assistant 120 may determine that she is referring to the firstitem on the meeting agenda (“Plan company event”). Automated assistant120 may also determine, e.g., by way of the entity tagger discussedpreviously, that “ball park” is a reference to a particular venueassociated with a particular sports team. While not depicted in FIG. 2B,in some implementations, at this point automated assistant 120 may causesecond client device 206 ₂ to display various information about the ballpark, such as pictures, a link to its website, information about thesports team, etc. The third participant 202 ₃ responds to the secondparticipant's statement by asking, “Good idea, what's its schedule?”Automated assistant 120, e.g., by way of the coreference resolverdescribed previously, may resolve the word “its” to the sports team itidentified previously. Then, automated assistant 120 may generate andsubmit a search query for the sports team's schedule, and may displayresponsive data on second client device 206 ₂, as is depicted in FIG.2B.

FIG. 2C depicts the same meeting at a later stage, after participants202 ₁₋₃ have concluded discussing the company event and are pivoting tothe next topic. The first participant 202 ₁ says “Good, looks like theevent is planned.” Automated assistant 120 may semantically process thisutterance and associate it with one of the meeting agenda items (e.g.,the first action item “Plan company event”). Additionally, automatedassistant 120 may determine, based on the semantic processing, that thisparticular agenda item has been addressed. Accordingly, automatedassistant 120 may render (or re-render) the meeting agenda on secondclient device 206 ₂ with the meeting agenda item “Plan company event”depicted as being completed, e.g., with the strikethrough depicted inFIG. 2C or another visual indicator (e.g., check box, font, etc.). Byrendering the meeting agenda at this point in the discussion when itappears participants 202 ₁₋₃ are transitioning to a different topic, theparticipants 202 ₁₋₃ are reminded of the next topic of discussion, whichin this case is to review a budget. This helps keep the meeting focusedand the participants on-topic.

FIG. 2D depicts one example of what might happen at the conclusion ofthe meeting. The third participant 202 ₃ says, “OK, let's get out ofhere.” As described previously, automated assistant 120 may semanticallyprocess this utterance to infer that the meeting has concluded.Consequently, in FIG. 2D, automated assistant 120 may take a number ofactions, including displaying a meeting summary on second client device206 ₂ and transitioning from conference mode to non-conference or normalmode. In this example, the displayed meeting summary includes a list oftopics discussed, which may or may not have been generated in part fromthe original meeting agenda. Here, the meeting summary includes outcomesof the meeting, including that the company event was planned and thebudget was reviewed. In addition, the meeting summary includes an actionitem that was discussed by the participants 202 ₁₋₃ during the meeting,e.g., in relation to the budget review, and detected semantically byautomated assistant 120.

In some implementations, a meeting summary such as that depicted in FIG.2D may be provided to one or more of the meeting participants, e.g., byway of email or file sharing. In some implementations in which it isdetermined by automated assistant 120 that a follow up meeting isplanned (e.g., from semantic processing of the discussion during themeeting or by way of a new calendar entry that is linked to the originalcalendar entry), the meeting summary may be saved and presented at thefollow meeting, e.g., as a meeting agenda. In some implementations,automated assistant 120 may automatically detect when two meetings arerelated and hence may share agenda and/or topics. For example, automatedassistant 120 may examine metadata associated with the multiple meetings(e.g., titles), or determine that the multiple meetings shareparticipants. In some implementations, automated assistant 120 maydetect patterns among multiple meetings that suggest a regularlyscheduled meeting, and may “carry over” a meeting summary across themultiple meetings.

In some implementations, automated assistant 120 may identify meetingparticipants in various ways, e.g., for purposes of pushing meetingagendas and/or summaries to those participants. As a simple example, acalendar entry may explicitly identify the meeting participants, whichautomated assistant 120 may use to determine email addresses of theparticipants. Additionally or alternatively, in some implementations,automated assistant 120 may be configured to perform speech recognitionto identify meeting participants, and then may match the identifiedparticipants to known user profiles. As another example, in someimplementations, the participant's may explicitly identify themselves,e.g., at the outset of the meeting as part of introductions, andautomated assistant 120 may detect the spoken names (and can, forinstance, add those names to the meeting summary).

In the example scenario of FIGS. 2A-D, all the meeting participants areco-located in a single location. However, as noted above this is notmeant to be limiting. FIG. 3 depicts an example of a meeting that occursbetween a first participant (not depicted) operating a first clientdevice 306 ₁ in the form of a desktop computer, and a second participant(not depicted) that is driving a vehicle 340 that includes an in-vehiclecomputing system that forms a second client device 306 ₂. For thisexample, it can be assumed that the first participant is able to speakor type free-form natural language input that is semantically processedby automated assistant 120, but that the second participant is limited(due to driving) to only providing spoken free-form natural languageinput. Automated assistant 120 is able to provide information visuallyand/or audibly at first client device 306 ₁ but only audibly at secondclient device 306 ₂, because visual output might distract a participantwho is driving.

Suppose the first participant at first client device 306 ₁ sayssomething during the meeting like “Do you want to go to Lexington thisweekend?”, and that the second (driving) user operating client device306 ₂ responds, “Maybe, depends on the weather.” Automated assistant 120may perform semantic processing on these utterances to generate one ormore search queries and retrieve information about Lexington andLexington's weather this weekend. Because the first participant isoperating first client device 306 ₁, which has a display, automatedassistant 120 may exercise relatively little discretion in selectingresponsive information to present. This is because the first participantis not known to be engaged in an activity like driving and becausevisual output is most likely less distracting. Accordingly, a wealth ofresponsive information is presented visual at first computing device 306₁, including other points of interest about Lexington itself, theweather in Lexington on Sunday, and points of interest within an hour ofLexington.

By contrast, automated assistant 120 is only able to push information tothe second participant driving the vehicle 340 using audio output.Accordingly, automated assistant 120 may be far more selective about theinformation it provides. For example, while the participants aregenerally discussing the location of Lexington, they have not explicitlyasked each other about points of interest. Accordingly, a relevancyscore associated with the various points of interest that are displayedon first client device 306 ₁ may not satisfy a minimum relevancy scorethat is used for a driving participant. Consequently, while the firstparticipant sees all the information about Lexington, the secondparticipant driving vehicle only hears the most relevant information,namely, the weather in Lexington on Sunday.

Thus it can be seen that in various implementations, automated assistant120 may adjust a relevancy threshold based on a context of a meetingparticipant. As another example, suppose the first user in FIG. 3 isoperating first client device 306 ₁ to do work (e.g., draft a document,work in a spreadsheet, perform research, etc.). In that context, itmight not be desirable to visually inundate or distract the firstparticipant with information related to the conversation. Accordingly,automated assistant 120 may adjust a relevancy threshold associated withto the first participant to be more closely aligned with the heightenedrelevancy threshold associated with the second, driving participant. Forexample, despite having display capabilities, because the firstparticipant is using the display for other purposes, automated assistant120 may elect to push information to the first participant audibly,rather than visual, to avoid distracting the first participant.

FIG. 4 is a flowchart illustrating an example method 400 according toimplementations disclosed herein. For convenience, the operations of theflow chart are described with reference to a system that performs theoperations. This system may include various components of variouscomputer systems, such as one or more components of computing systemsthat implement automated assistant 120. Moreover, while operations ofmethod 400 are shown in a particular order, this is not meant to belimiting. One or more operations may be reordered, omitted or added.

At block 402, the system may set an automated assistant 120 implementedat least in part on one or more conference computing devices to aconference mode in which the automated assistant performs speech-to-textprocessing on multiple distinct spoken utterances without requiringexplicit invocation of the automated assistant prior to each of themultiple distinct spoken utterances. As described herein, in variousimplementations, the multiple distinct spoken utterances may be providedby multiple human participants during a meeting between the multipleparticipants.

At block 404, the system may automatically perform semantic processingon first text generated from the speech-to-text processing of one ormore of the multiple distinct spoken utterances. In particular, thesemantic processing may be performed without explicit participantinvocation. And in fact, in various implementations, the system mayperform semantic processing on text generated from all participantutterances. If a particular participant utterance is indecipherable, itmay not be possible to convert the speech to text, in which caseautomated assistant 120 takes no action. If a particular participantutterance is decipherable but when semantically processed does not yieldinformation that is relevant to the meeting discussion (e.g., relevancyscore fails to satisfy relevancy threshold), automated assistant 120 maytake no action on the retrieved information. However if the informationretrieved based on the semantic processing satisfies some criterion,such as a relevancy threshold, at block 406, the system may generatepertinent data (e.g., natural language output) based on the informationobtained as a result of the semantic processing and output (at block408) that pertinent data to one or more of the multiple participants atone or more of the conference computing devices.

At block 410, the system may determine that the meeting has concluded.As noted above, this determination may be made in response to anexplicit command from a participant (“OK Assistant, let's conclude themeeting”), inferred from an utterance of a user (“This meeting isadjourned”), or made in response to other user input, such as tapping asurface of a standalone interactive speaker that is being used as aconference computing device. In response to the determination of block410, at block 412, the system may set automated assistant 120 to anon-conference mode in which the automated assistant requires invocationprior to performing speech-to-text processing on individual spokenutterances.

At block 414, in some implementations, the system may generate, e.g.,based on semantic processing of multiple utterances provided by themeeting participants during the meeting, a meeting summary. As notedabove, the meeting summary may include things like topics discussed,action items (created, resolved, modified, etc.), participants, and/or apartial or complete transcript of the meeting. In some implementations,the transcript may be annotated with or otherwise include not only theparticipants' utterances, but also any information injected into themeeting by automated assistant 120.

FIG. 5 is a block diagram of an example computing device 510 that mayoptionally be utilized to perform one or more aspects of techniquesdescribed herein. Computing device 510 typically includes at least oneprocessor 514 which communicates with a number of peripheral devices viabus subsystem 512. These peripheral devices may include a storagesubsystem 524, including, for example, a memory subsystem 525 and a filestorage subsystem 526, user interface output devices 520, user interfaceinput devices 522, and a network interface subsystem 516. The input andoutput devices allow user interaction with computing device 510. Networkinterface subsystem 516 provides an interface to outside networks and iscoupled to corresponding interface devices in other computing devices.

User interface input devices 522 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computing device 510 or onto a communication network.

User interface output devices 520 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computing device 510 to the user or to another machine or computingdevice.

Storage subsystem 524 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the storage subsystem 524 may include the logic toperform selected aspects of the method of FIG. 4 , as well as toimplement various components depicted in FIG. 1 .

These software modules are generally executed by processor 514 alone orin combination with other processors. Memory 525 used in the storagesubsystem 524 can include a number of memories including a main randomaccess memory (RAM) 530 for storage of instructions and data duringprogram execution and a read only memory (ROM) 532 in which fixedinstructions are stored. A file storage subsystem 526 can providepersistent storage for program and data files, and may include a harddisk drive, a floppy disk drive along with associated removable media, aCD-ROM drive, an optical drive, or removable media cartridges. Themodules implementing the functionality of certain implementations may bestored by file storage subsystem 526 in the storage subsystem 524, or inother machines accessible by the processor(s) 514.

Bus subsystem 512 provides a mechanism for letting the variouscomponents and subsystems of computing device 510 communicate with eachother as intended. Although bus subsystem 512 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computing device 510 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. Due to the ever-changing natureof computers and networks, the description of computing device 510depicted in FIG. 5 is intended only as a specific example for purposesof illustrating some implementations. Many other configurations ofcomputing device 510 are possible having more or fewer components thanthe computing device depicted in FIG. 5 .

In situations in which certain implementations discussed herein maycollect or use personal information about users (e.g., user dataextracted from other electronic communications, information about auser's social network, a user's location, a user's time, a user'sbiometric information, and a user's activities and demographicinformation, relationships between users, etc.), users are provided withone or more opportunities to control whether information is collected,whether the personal information is stored, whether the personalinformation is used, and how the information is collected about theuser, stored and used. That is, the systems and methods discussed hereincollect, store and/or use user personal information only upon receivingexplicit authorization from the relevant users to do so.

For example, a user is provided with control over whether programs orfeatures collect user information about that particular user or otherusers relevant to the program or feature. Each user for which personalinformation is to be collected is presented with one or more options toallow control over the information collection relevant to that user, toprovide permission or authorization as to whether the information iscollected and as to which portions of the information are to becollected. For example, users can be provided with one or more suchcontrol options over a communication network. In addition, certain datamay be treated in one or more ways before it is stored or used so thatpersonally identifiable information is removed. As one example, a user'sidentity may be treated so that no personally identifiable informationcan be determined. As another example, a user's geographic location maybe generalized to a larger region so that the user's particular locationcannot be determined.

While several implementations have been described and illustratedherein, a variety of other means and/or structures for performing thefunction and/or obtaining the results and/or one or more of theadvantages described herein may be utilized, and each of such variationsand/or modifications is deemed to be within the scope of theimplementations described herein. More generally, all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific implementationsdescribed herein. It is, therefore, to be understood that the foregoingimplementations are presented by way of example only and that, withinthe scope of the appended claims and equivalents thereto,implementations may be practiced otherwise than as specificallydescribed and claimed. Implementations of the present disclosure aredirected to each individual feature, system, article, material, kit,and/or method described herein. In addition, any combination of two ormore such features, systems, articles, materials, kits, and/or methods,if such features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

What is claimed is:
 1. A method implemented using one or moreprocessors, comprising: during a first conversation between multipleparticipants, setting an automated assistant implemented at least inpart on one or more conference computing devices to a conference mode inwhich the automated assistant performs semantic processing on multipledistinct spoken utterances without requiring explicit invocation of theautomated assistant prior to each of the multiple distinct spokenutterances; detecting, by the automated assistant based on the semanticprocessing of the multiple distinct utterances, one or more topicsdiscussed by one or more of the multiple participants during the firstconversation; storing one or more of the topics discussed by one or moreof the multiple participants during the first conversation for useduring one or more subsequent conversations; determining that a secondconversation that occurs subsequent to the first conversation is relatedto the first conversation; in response to the determining, and duringthe second conversation that occurs subsequent to the firstconversation, retrieving one or more of the stored topics; andgenerating, by the automated assistant, data indicative of the one ormore retrieved topics; and outputting, by the automated assistant at oneor more of the conference computing devices, the data indicative of theone or more retrieved topics.
 2. The method of claim 1, wherein thedetermining comprises detecting an overlap between the multipleparticipants of the first conversation and at least one participant inthe second conversation that occurs subsequent to the firstconversation.
 3. The method of claim 2, wherein the overlap is detectedbased on calendar entries associated with the first and secondconversations.
 4. The method of claim 1, wherein the determining isbased on titles assigned to the first and second conversations.
 5. Themethod of claim 1, wherein the determining is based on one or moredocuments associated with the first and second conversations.
 6. Themethod of claim 5, further comprising generating an initial dialogcontext for the second conversation based on one or more of thedocuments.
 7. The method of claim 1, wherein the one or more of thetopics discussed by one or more of the multiple participants comprise aplurality of action items, and the method further comprises: determiningthat a given action item of the plurality of action items was addressedduring a time interval between the first and second conversations; andrefraining from including the given action item in the data indicativeof the one or more retrieved topics.
 8. The method of claim 1, whereinthe one or more of the topics discussed by one or more of the multipleparticipants comprise a plurality of action items, and the methodfurther comprises, in response to a determination that a given actionitem of the plurality of action items was not addressed during the firstconversation or during a time interval between the first and secondconversations, including the given action item in the data indicative ofthe one or more retrieved topics.
 9. A system comprising one or moreprocessors and memory storing instructions that, in response toexecution of the instructions, cause the one or more processors to:during a first conversation between multiple participants, set anautomated assistant implemented at least in part on one or moreconference computing devices to a conference mode in which the automatedassistant performs semantic processing on multiple distinct spokenutterances without requiring explicit invocation of the automatedassistant prior to each of the multiple distinct spoken utterances;detect, by the automated assistant based on the semantic processing ofthe multiple distinct utterances, one or more topics discussed by one ormore of the multiple participants during the first conversation; storeone or more of the topics discussed by one or more of the multipleparticipants during the first conversation for use during one or moresubsequent conversations; determine that a second conversation thatoccurs subsequent to the first conversation is related to the firstconversation; in response to the determination that the secondconversation is related to the first conversation, and during the secondconversation that occurs subsequent to the first conversation, retrieveone or more of the stored topics; and generate, by the automatedassistant, data indicative of the one or more retrieved topics; andoutput, by the automated assistant at one or more of the conferencecomputing devices, the data indicative of the one or more retrievedtopics.
 9. The system of claim 8, wherein the determination that thesecond conversation is related to the first conversation is made basedon detection of an overlap between the multiple participants of thefirst conversation and at least one participant in the secondconversation that occurs subsequent to the first conversation.
 10. Thesystem of claim 9, wherein the overlap is detected based on calendarentries associated with the first and second conversations.
 11. Thesystem of claim 8, wherein the determination that the secondconversation is related to the first conversation is based on titlesassigned to the first and second conversations.
 12. The system of claim8, wherein the determination that the second conversation is related tothe first conversation is based on one or more documents associated withthe first and second conversations.
 13. The system of claim 12, furthercomprising instructions to generate an initial dialog context for thesecond conversation based on one or more of the documents.
 14. Thesystem of claim 8, wherein the one or more of the topics discussed byone or more of the multiple participants comprise a plurality of actionitems, and the system further comprises instructions to: determine thata given action item of the plurality of action items was addressedduring a time interval between the first and second conversations; andrefrain from including the given action item in the data indicative ofthe one or more retrieved topics.
 15. The system of claim 8, wherein theone or more of the topics discussed by one or more of the multipleparticipants comprise a plurality of action items, and the systemfurther comprises instructions to, in response to a determination that agiven action item of the plurality of action items was not addressedduring the first conversation or during a time interval between thefirst and second conversations, include the given action item in thedata indicative of the one or more retrieved topics.
 16. Anon-transitory computer-readable medium comprising instructions that, inresponse to execution of the instructions by a processor, cause theprocessor to: during a first conversation between multiple participants,set an automated assistant implemented at least in part on one or moreconference computing devices to a conference mode in which the automatedassistant performs semantic processing on multiple distinct spokenutterances without requiring explicit invocation of the automatedassistant prior to each of the multiple distinct spoken utterances;detect, by the automated assistant based on the semantic processing ofthe multiple distinct utterances, one or more topics discussed by one ormore of the multiple participants during the first conversation; storeone or more of the topics discussed by one or more of the multipleparticipants during the first conversation for use during one or moresubsequent conversations; determine that a second conversation thatoccurs subsequent to the first conversation is related to the firstconversation; in response to the determination that the secondconversation is related to the first conversation, and during the secondconversation that occurs subsequent to the first conversation, retrieveone or more of the stored topics; and generate, by the automatedassistant, data indicative of the one or more retrieved topics; andoutput, by the automated assistant at one or more of the conferencecomputing devices, the data indicative of the one or more retrievedtopics.
 17. The non-transitory computer-readable medium of claim 16,wherein the determination that the second conversation is related to thefirst conversation is made based on detection of an overlap between themultiple participants of the first conversation and at least oneparticipant in the second conversation that occurs subsequent to thefirst conversation.
 18. The non-transitory computer-readable medium ofclaim 17, wherein the overlap is detected based on calendar entriesassociated with the first and second conversations.
 19. Thenon-transitory computer-readable medium of claim 16, wherein thedetermination that the second conversation is related to the firstconversation is based on titles assigned to the first and secondconversations.
 20. The non-transitory computer-readable medium of claim16, wherein the determination that the second conversation is related tothe first conversation is based on one or more documents associated withthe first and second conversations.